Effective communication of emotion relies on a complex interplay of complementary non-verbal channels involving facial and vocal changes. In addition to emotion expression, imitation of emotion also plays a major role in understanding emotion by replicating it Effective use of these face-voice channels and identification of the changes therein is impaired in many patients suffering from neuropsychiatric disorders Quantification of such deficits will help advance basic and clinical research, eventually leading to improved diagnostic accuracy and assessment of treatment effects. Current methodology relies most heavily on clinical ratings that may be subjective and visual rater dependent. The limited automated methods available for facial expression and voice analysis, are able to recognize the emotion, but have been fairly unsuccessful in quantifying the degree of emotion. This has created the need for objective automated methods of emotion evaluation that can quantify emotion, supplement clinical ratings and aid in diagnosis decisions. This project seeks to address these issues by developing and validating advanced automated computerized tools that can objectively and reliably quantify multimodal affect processing. This comprehensive quantified assessment of emotion expression and imitation using single or combined audio-visual modalities of facial expression and voice, will determine the impact of each channel on emotion understanding and on identification of affect related differences between patient-control groups, thereby complementing and augmenting current clinical symptom rating scales. The measures we produce will be easy to employ and could facilitate large-scale studies measuring impairment in affect and affect change across disorders that lead to impaired affect. In Aim 1 we will develop and validate classifier-based methods for facial affect analysis based on automated temporal action unit profiles, for quantifying facial emotion expression and imitation in the presence of speech. In Aim 2, we develop and validate emotion classifiers based on the spectral and prosodic features extracted from the acoustic signal. These will quantify emotion in expressed and imitated voice. Finally in Aim 3, we will create a video-based automated emotion expression quantification system that fuses facial and voice features identified in Aims 1 and 2. The population-specific set of face-voice classifiers designed will best elucidate patient-control differences in expression and imitation. Results will be compared to to clinical ratings. We expect that on successful completion of the project we will have an integrated collection of objective facial and speech expression analysis tools, usable by neuropsychiatrists to quantify the degree of emotion impairment and study treatment effects. We expect our methods to influence procedures used for diagnosing schizophrenia and perhaps affective disorders and autism spectrum disorders. The methods will be generic and could be further extended to other neuropsychiatric or neurological conditions that cause deficits in emotional expressiveness.